10 Tips for Creating a Foundation Model for India

As we are discussing creating  Large Language Model (LLM) for India instead of using LLM created by American and Chinese companies I thought of sharing some tips to build a AI with a difference. Here are 10 key tips for building a strong foundation model for India, considering its unique linguistic, cultural, and infrastructural diversity:

 

India

1. Multilingual Training Data

   • India has 22 official languages and hundreds of dialects. A robust foundation model must incorporate high-quality, diverse, and regionally balanced data across multiple languages.

2. Bias Mitigation in Data

   • Socioeconomic, gender, and caste-based biases exist in many datasets. Implement bias detection and fairness checks to ensure inclusive AI outputs.
     
     
     
     

3. Incorporation of Local Knowledge

   • AI should integrate indigenous knowledge, traditional practices, and cultural references to provide more accurate and contextually relevant responses. 
     
     
     
     

4. Handling Low-Resource Languages

   • Many Indian languages lack sufficient digital data. Utilize transfer learning, synthetic data generation, and crowd-sourced datasets to enhance AI capabilities.
     
     
     

5. Adaptation to Regional Variations

   • Words and phrases can have different meanings across states. Training should include localized NLP models to understand context-specific variations.

6. Data Quality and Noise Reduction

   • Ensure datasets are accurate, well-annotated, and free from misinformation. Remove noisy or misleading data from social media sources.

7. Infrastructure and Scalability

   • Indian users access AI on a wide range of devices, from high-end smartphones to basic feature phones. Optimize the model for efficiency and offline accessibility.

8. Legal and Ethical Compliance

   • Follow India’s data protection laws (such as the DPDP Act) and ensure responsible AI practices to prevent misuse and protect privacy.

9. Customization for Sectors

   • Train AI specifically for key Indian sectors like agriculture, healthcare, education, and governance to provide domain-specific solutions.

10. Community Involvement & Open-Source Collaboration

• Engage with local AI researchers, linguists, and developers to create an open, collaborative model that truly represents India's diversity.

Leveraging Prompt Engineering for Business Agility

Introduction

This tutorial is aimed at beginners who are curious about prompt engineering, offering a comprehensive, hands-on overview of the fundamentals, techniques, and step-by-step examples to help you practice and master the skill.

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What is Prompt Engineering?

Prompt engineering is the process of crafting inputs to AI models in a way that yields the most useful, relevant, and accurate results. Because LLMs generate responses based on patterns learned from massive datasets, the way you ask a question can significantly influence the answer.

In essence, prompt engineering is about:

• Understanding how LLMs interpret and respond to input.

• Designing prompts to guide the model's behavior.

• Iterating and refining prompts to improve outcomes.

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Why Prompt Engineering Matters

AI models are highly capable, but they are not mind readers. They depend entirely on the text provided. Subtle variations in phrasing, tone, specificity, or structure can change the results dramatically.

Benefits of good prompt engineering include:

• More accurate and relevant outputs.

• Reduced hallucinations or fabricated content.

• Increased efficiency in achieving results.

• Better alignment with business, educational, or creative goals.

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Basic Principles of Prompt Engineering

1. Clarity

   • Clear prompts produce clearer responses.

   • Avoid ambiguity.

2. Specificity

   • The more specific the prompt, the better the output.

   • Specify the format, tone, length, or point of view if needed.

3. Contextualization

   • Provide background or context to help the model generate more informed responses.

4. Instructional Language

   • Use imperative or guiding language: "List", "Summarize", "Compare", etc.

5. Iteration

   • Refine and reword prompts based on outputs.

   • Use feedback loops.

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Step-by-Step Prompt Engineering Examples

Example 1: Summarizing Text

Task: Summarize a paragraph about renewable energy.

Input Text: "Renewable energy sources such as solar, wind, and hydroelectric power have seen increased adoption across the world due to their sustainability and low environmental impact. These sources help reduce carbon emissions and reliance on fossil fuels."

Basic Prompt: "Summarize the paragraph."

Output: "Renewable energy sources like solar, wind, and hydro are sustainable and reduce carbon emissions and fossil fuel use."

Improved Prompt: "Summarize the following paragraph in one sentence using simple language for a high school student."

Output: "Renewable energy like solar and wind is good for the environment and helps us use less fossil fuel."

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Example 2: Rewriting in a Specific Tone

Task: Rewrite a message to sound more professional.

Original Text: "Hey, I need that report by tomorrow or we're going to miss the deadline."

Prompt: "Rewrite the following message to sound professional and polite: 'Hey, I need that report by tomorrow or we're going to miss the deadline.'"

Output: "Could you please share the report by tomorrow? It's essential to meet our project deadline."

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Example 3: Code Generation

Task: Generate a Python function to calculate the factorial of a number.

Prompt: "Write a Python function to calculate the factorial of a number."

Output:

def factorial(n):      if n == 0 or n == 1:          return 1      else:          return n * factorial(n - 1)  

Improved Prompt: "Write a Python function using recursion to calculate the factorial of a number. Include comments for beginners."

Output:

# This function calculates the factorial of a number using recursion  def factorial(n):      # Base case: factorial of 0 or 1 is 1      if n == 0 or n == 1:          return 1      # Recursive case      else:          return n * factorial(n - 1)  

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Example 4: Role-based Prompting

Task: Get travel tips from a local perspective.

Prompt: "You are a local from Tokyo. What should I do if I have just 2 days to explore the city?"

Output: "If you have only 2 days in Tokyo, start with Asakusa to visit Senso-ji Temple, then head to Shibuya for the famous crossing and local food. Day 2: Visit Meiji Shrine, explore Harajuku and Omotesando for shopping, and end with a night view from Tokyo Tower."

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Example 5: Step-by-Step Reasoning

Task: Solve a basic math word problem.

Prompt: "If a pencil costs $2 and a notebook costs $5, how much would 3 pencils and 2 notebooks cost? Show your steps."

Output: "Step 1: Cost of 3 pencils = 3 x $2 = $6 Step 2: Cost of 2 notebooks = 2 x $5 = $10 Step 3: Total cost = $6 + $10 = $16"

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Techniques Recap with Examples

Technique	Description	Example Prompt
Zero-shot	No examples provided	"Translate to Spanish: 'Good morning.'"
Few-shot	A few examples provided	"Translate: 1. Hello -> Hola, 2. Cat -> Gato, 3. Dog ->"
Chain-of-thought	Reasoning steps included	"How many legs do 3 dogs and 2 cats have? Show steps."
Role-based	Model acts in a specific role	"You are a nutritionist. Suggest a healthy breakfast."
Output formatting	Specify bullet points or tables	"List pros and cons of remote work in bullet points."

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Tips and Best Practices

1. Be Iterative – Start with simple prompts and refine them.

2. Use Constraints – Set output length, format, or style.

3. Test Edge Cases – Check how the model handles unexpected or incorrect input.

4. Chain Prompts Together – For complex tasks, break them into smaller sub-prompts.

5. Maintain Context – Provide background when needed.

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Common Mistakes to Avoid

• Being too vague or general.

• Using long, complex sentences without structure.

• Not checking model output for factual errors.

• Relying on a single prompt for a complex task.

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Applications of Prompt Engineering

1. Software Development – Generate code, test cases, documentation.

2. Education – Create lesson plans, quizzes, explain topics.

3. Marketing – Write copy, generate ideas, refine slogans.

4. Customer Support – Draft replies, suggest solutions.

5. Creative Writing – Develop stories, characters, plot ideas.

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Future of Prompt Engineering

• Prompt Libraries – Standard reusable prompts for industries.

• AI-Generated Prompts – Meta-models that optimize prompts.

• Natural Language APIs – Interfaces where prompt engineering is embedded.

• Prompt GUIs – Visual builders for non-technical users.

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Conclusion

Prompt engineering is the bridge between human intent and machine intelligence. Mastering this skill allows you to unlock the true potential of AI models. Through clarity, specificity, structure, and creativity, you can design prompts that deliver powerful, practical, and precise results.

Practice with the examples in this tutorial, experiment with variations, and you'll become proficient in crafting prompts that give expected results.